Competitive economies motivate business managers and other users to obtain maximum value from their investments for Corporate Performance Management (CPM) tools, such as Business Intelligence (BI) tools, that are used to manage business oriented data and metadata. These CPM tools provide authored reports or authored drill-through targets to link content together. Users often encounter similar problems in finding important reports or relevant data or drilling to related content if it was not previously authored.
Traditional search technologies often provide incomplete or irrelevant results in the CPM environments. There are metadata search tools that run against relational databases. They can fail to find relevant data since they only search databases and do not leverage a customer's investment in CPM tools and applications. Relying on authored drill-through targets can also be problematic as new cube, reports, metrics or plans are added since new drill targets are not always kept up-to-date. Users can have difficulties moving seamlessly between CPM tools or applications, particularly when CPM applications are created by different individuals or departments.
It is therefore desirable to provide a mechanism that allows more effective searches of business oriented metadata content.
There exist search engines that use a full-text index combined with statistical methods to create ordered search results. An example of such a search engine is page ranking that is described in U.S. Pat. No. 6,526,440 issued to Bharat. However, these search engines are not sufficient to search complex data like business oriented metadata since they rely on ranking algorithms that work with data found primarily in the Global Internet and not inside a business.
Some search engines use taxonomies to improve results. Creation of taxonomies has been carried out by a manual process or by an automated process based on advanced linguistic analysis. However, business taxonomies are difficult and expensive to build manually. Also, linguistic analysis are often complicated and thus prone to result in inaccurate outcome.
It is therefore desirable to provide a system that manages business taxonomies automatically without the need for complicated and potentially inaccurate linguistic analysis.